Web Searching: A Quality Measurement Perspective

The purpose of this paper is to describe various quality measures for search engines and to ask whether these are suitable. We especially focus on user needs and their use of web search engines. The paper presents an extensive literature review and a first quality measurement model, as well. Findings include that search engine quality can not be measured by just retrieval effectiveness (the quality of the results), but should also consider index quality, the quality of the search features and search engine usability. For each of these sections, empirical results from studies conducted in the past, as well as from our own research are presented. These results have implications for the evaluation of search engines and for the development of better search systems that give the user the best possible search experience

An effective location-based information filtering system on mobile devices

As mobile devices evolve, research on providing location-based services attract researchers interest. A location-based service recommends information based on users geographical location provided by a mobile device. Mobile devices are engaged with users daily activities and lots of information and services are requested by users, so suggesting the proper information on mobile devices that reflects user preferences becomes more and more difficult. Lots of recent studies have tried to tackle this issue but most of them are not successful because of reasons such as using large datasets or making suggestions based on dynamically collected ratings within different groups instead of focusing on individuals. In this paper, we propose a location based information filtering system that exposes users preferences using Bayesian inferences. A Bayesian network is constructed with conditional probability table while Users characteristics and location data are gathered by using the mobile device. After preprocessing those data, the system integrates that information and uses time to produce the most accurate suggestions. We collected a dataset from 20 restaurants in Malaysia and we gathered behavioral data from two registered users for 7 days. We conducted experiment on the dataset to demonstrate effectiveness of the proposed system and to explain user preferences

Improved Many-Objective Optimization Algorithms for the 3D Indoor Deployment Problem

International audienceCompared with the two-dimensional deployment, the three-dimensional deployment of sensor networks is more challenging. We studied the problem of 3D repositioning of sensor nodes in wireless sensor networks. We aim essentially to add a set of nodes to the initial architecture. The positions of the added nodes are determined by the proposed algorithms while optimizing a set of objectives. In this paper, we suggest two main contributions. The first one is an analysis contribution where the modelling of the problem is given and a set of modifications is incorporated on the tested multi-objective evolutionary algorithms to resolve the issues encountered when resolving many-objective problems. These modifications concern essentially an adaptive mutation and recombination operators with neighbourhood mating restrictions, the use of a multiple scalarizing functions concept and the incorporation of the reduction in dimensionality. The second contribution is an application one, where an experimental study on real testbeds is detailed to test the behaviour of the enhanced algorithms on a real-world context. Experimental tests followed by numerical results prove the efficiency of the proposed modifications against original algorithms